Clase 8- Inspeccionar Data Frames

Fecha de publicación

11 de noviembre de 2024

Objetivos de la Actividad

  • Presentar operadores lógicos y trabajar con ellos

  • Acceder a elementos de vectores y DF’s según condiciones lógicas

  • Inspeccionar valores, rangos y estructura de una DF

Tabla de Contingencia

Conteo de frecuencia de un dato categórico

table(mtcars$cyl)

 4  6  8 
11  7 14 

Tabla de Contingencia Cruzada

Múltiples categorías

cyl Number of cylinders
vs Engine (0 = V-shaped, 1 = straight)
table(mtcars$cyl, mtcars$vs)
   
     0  1
  4  1 10
  6  3  4
  8 14  0

Tabla Resumen Estadístico

Representar los valores mínimo y máximo, primer y tercer cuartil, media, promedio de un vector o data frame.

summary(mtcars$mpg)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  10.40   15.43   19.20   20.09   22.80   33.90 

Tabla Resumen Estadístico - cont

La salida de función summary cambia según el objeto que estemos trabajando

summary(mtcars)
      mpg             cyl             disp             hp       
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
 Median :19.20   Median :6.000   Median :196.3   Median :123.0  
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  
      drat             wt             qsec             vs        
 Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  
 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  
 Median :3.695   Median :3.325   Median :17.71   Median :0.0000  
 Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  
 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  
 Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  
       am              gear            carb      
 Min.   :0.0000   Min.   :3.000   Min.   :1.000  
 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  
 Median :0.0000   Median :4.000   Median :2.000  
 Mean   :0.4062   Mean   :3.688   Mean   :2.812  
 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  
 Max.   :1.0000   Max.   :5.000   Max.   :8.000  

Operadores Lógicos

  • \(>\) (mayor a)
  • \(>=\) (mayor o igual a)
  • \(<\) (menor a)
  • \(<=\) (menor o igual a)
  • \(==\) (igual a)
  • \(!=\) (distinto a)
  • & (y)
  • \(|\) (o)

Uso de Operadores Lógicos en un Vector

Vector seleccionado “millas por galón” mpg

mtcars$mpg
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4
[16] 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7
[31] 15.0 21.4

Vector Lógico con valores que Cumplen una Condición

Mayor que un valor dado

  mtcars$mpg>19.20
 [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
[25] FALSE  TRUE  TRUE  TRUE FALSE  TRUE FALSE  TRUE

Mayor que el valor que retorna una función

mtcars$mpg>mean(mtcars$mpg)
 [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE
[25] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE

Asignación valores a variables

  • variable con promedio

  • creación DF

  • evaluar valores que cumplen ambas condiciones

promedio_mpg <- mean(mtcars$mpg)
valor_3ercuartil <- 33.90 

df_mpg <- data.frame(mpg= mtcars$mpg,
                     may_mean= mtcars$mpg>promedio_mpg,
                     men_3cuar= mtcars$mpg<valor_3ercuartil )

Inspección df

df_mpg
    mpg may_mean men_3cuar
1  21.0     TRUE      TRUE
2  21.0     TRUE      TRUE
3  22.8     TRUE      TRUE
4  21.4     TRUE      TRUE
5  18.7    FALSE      TRUE
6  18.1    FALSE      TRUE
7  14.3    FALSE      TRUE
8  24.4     TRUE      TRUE
9  22.8     TRUE      TRUE
10 19.2    FALSE      TRUE
11 17.8    FALSE      TRUE
12 16.4    FALSE      TRUE
13 17.3    FALSE      TRUE
14 15.2    FALSE      TRUE
15 10.4    FALSE      TRUE
16 10.4    FALSE      TRUE
17 14.7    FALSE      TRUE
18 32.4     TRUE      TRUE
19 30.4     TRUE      TRUE
20 33.9     TRUE     FALSE
21 21.5     TRUE      TRUE
22 15.5    FALSE      TRUE
23 15.2    FALSE      TRUE
24 13.3    FALSE      TRUE
25 19.2    FALSE      TRUE
26 27.3     TRUE      TRUE
27 26.0     TRUE      TRUE
28 30.4     TRUE      TRUE
29 15.8    FALSE      TRUE
30 19.7    FALSE      TRUE
31 15.0    FALSE      TRUE
32 21.4     TRUE      TRUE

Forma Vectorizada:

Se aplica el condicional lógico sobre el elemento i del vector analizado teniendo de resultado un vector del mismo length del vector de entrada.

Por ejemplo, si i vale 3, se compara si 22.8 es mayor que el promedio_mpg y si es menor que valor_3ercuartil

mtcars$mpg>promedio_mpg & mtcars$mpg<valor_3ercuartil 
 [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE
[13] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE FALSE
[25] FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE

Múltiples Condiciones

Operador & (y)

mtcars$mpg[mtcars$mpg>promedio_mpg & mtcars$mpg<valor_3ercuartil ]
 [1] 21.0 21.0 22.8 21.4 24.4 22.8 32.4 30.4 21.5 27.3 26.0 30.4 21.4

Operador == doble igualdad (👀 es distinto a asignación)

 which (mtcars$mpg == 19.2  )
[1] 10 25

which indica el índice de los elementos extraídos según una condición

mtcars[ mtcars$mpg==21, ]
              mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4      21   6  160 110  3.9 2.620 16.46  0  1    4    4
Mazda RX4 Wag  21   6  160 110  3.9 2.875 17.02  0  1    4    4

Operador != diferente

mtcars[mtcars$mpg!=21,c(1,4:6)]
                     mpg  hp drat    wt
Datsun 710          22.8  93 3.85 2.320
Hornet 4 Drive      21.4 110 3.08 3.215
Hornet Sportabout   18.7 175 3.15 3.440
Valiant             18.1 105 2.76 3.460
Duster 360          14.3 245 3.21 3.570
Merc 240D           24.4  62 3.69 3.190
Merc 230            22.8  95 3.92 3.150
Merc 280            19.2 123 3.92 3.440
Merc 280C           17.8 123 3.92 3.440
Merc 450SE          16.4 180 3.07 4.070
Merc 450SL          17.3 180 3.07 3.730
Merc 450SLC         15.2 180 3.07 3.780
Cadillac Fleetwood  10.4 205 2.93 5.250
Lincoln Continental 10.4 215 3.00 5.424
Chrysler Imperial   14.7 230 3.23 5.345
Fiat 128            32.4  66 4.08 2.200
Honda Civic         30.4  52 4.93 1.615
Toyota Corolla      33.9  65 4.22 1.835
Toyota Corona       21.5  97 3.70 2.465
Dodge Challenger    15.5 150 2.76 3.520
AMC Javelin         15.2 150 3.15 3.435
Camaro Z28          13.3 245 3.73 3.840
Pontiac Firebird    19.2 175 3.08 3.845
Fiat X1-9           27.3  66 4.08 1.935
Porsche 914-2       26.0  91 4.43 2.140
Lotus Europa        30.4 113 3.77 1.513
Ford Pantera L      15.8 264 4.22 3.170
Ferrari Dino        19.7 175 3.62 2.770
Maserati Bora       15.0 335 3.54 3.570
Volvo 142E          21.4 109 4.11 2.780

Operador | or (o)

mtcars[which(mtcars$mpg==21 | mtcars$mpg==22.8),1:3]
               mpg cyl  disp
Mazda RX4     21.0   6 160.0
Mazda RX4 Wag 21.0   6 160.0
Datsun 710    22.8   4 108.0
Merc 230      22.8   4 140.8

Data Frame a Trabajar

# View(datasets::LifeCycleSavings)
Sobre el contenido de la df: según la hipótesis del ahorro a lo largo del ciclo vital desarrollada por Franco Modigliani, el coeficiente de ahorro (ahorro personal agregado dividido por la renta disponible) se explica por la renta disponible per cápita, la tasa porcentual de variación de la renta disponible per cápita y dos variables demográficas: el porcentaje de población menor de 15 años y el porcentaje de población mayor de 75 años. Los datos se promedian a lo largo de la década 1960-1970 para eliminar el ciclo económico u otras fluctuaciones a corto plazo

Asignar a una variable la DF

df_ahorro <- datasets::LifeCycleSavings
head(df_ahorro, 8)
             sr pop15 pop75     dpi ddpi
Australia 11.43 29.35  2.87 2329.68 2.87
Austria   12.07 23.32  4.41 1507.99 3.93
Belgium   13.17 23.80  4.43 2108.47 3.82
Bolivia    5.75 41.89  1.67  189.13 0.22
Brazil    12.88 42.19  0.83  728.47 4.56
Canada     8.79 31.72  2.85 2982.88 2.43
Chile      0.60 39.74  1.34  662.86 2.67
China     11.90 44.75  0.67  289.52 6.51

Inspección Data Frame

Dimensiones

dim(df_ahorro)
[1] 50  5

Número de columnas

ncol(df_ahorro)
[1] 5

Número de filas

nrow(df_ahorro)
[1] 50

Nombres de las columnas

colnames(df_ahorro)
[1] "sr"    "pop15" "pop75" "dpi"   "ddpi" 

Nombres de las filas

rownames(df_ahorro)
 [1] "Australia"      "Austria"        "Belgium"        "Bolivia"       
 [5] "Brazil"         "Canada"         "Chile"          "China"         
 [9] "Colombia"       "Costa Rica"     "Denmark"        "Ecuador"       
[13] "Finland"        "France"         "Germany"        "Greece"        
[17] "Guatamala"      "Honduras"       "Iceland"        "India"         
[21] "Ireland"        "Italy"          "Japan"          "Korea"         
[25] "Luxembourg"     "Malta"          "Norway"         "Netherlands"   
[29] "New Zealand"    "Nicaragua"      "Panama"         "Paraguay"      
[33] "Peru"           "Philippines"    "Portugal"       "South Africa"  
[37] "South Rhodesia" "Spain"          "Sweden"         "Switzerland"   
[41] "Turkey"         "Tunisia"        "United Kingdom" "United States" 
[45] "Venezuela"      "Zambia"         "Jamaica"        "Uruguay"       
[49] "Libya"          "Malaysia"      

Estructura de un objeto

str(df_ahorro)
'data.frame':   50 obs. of  5 variables:
 $ sr   : num  11.43 12.07 13.17 5.75 12.88 ...
 $ pop15: num  29.4 23.3 23.8 41.9 42.2 ...
 $ pop75: num  2.87 4.41 4.43 1.67 0.83 2.85 1.34 0.67 1.06 1.14 ...
 $ dpi  : num  2330 1508 2108 189 728 ...
 $ ddpi : num  2.87 3.93 3.82 0.22 4.56 2.43 2.67 6.51 3.08 2.8 ...

Resumen Estadístico

summary(df_ahorro)
       sr             pop15           pop75            dpi         
 Min.   : 0.600   Min.   :21.44   Min.   :0.560   Min.   :  88.94  
 1st Qu.: 6.970   1st Qu.:26.21   1st Qu.:1.125   1st Qu.: 288.21  
 Median :10.510   Median :32.58   Median :2.175   Median : 695.66  
 Mean   : 9.671   Mean   :35.09   Mean   :2.293   Mean   :1106.76  
 3rd Qu.:12.617   3rd Qu.:44.06   3rd Qu.:3.325   3rd Qu.:1795.62  
 Max.   :21.100   Max.   :47.64   Max.   :4.700   Max.   :4001.89  
      ddpi       
 Min.   : 0.220  
 1st Qu.: 2.002  
 Median : 3.000  
 Mean   : 3.758  
 3rd Qu.: 4.478  
 Max.   :16.710  

Crear columna con nombre países

Revisar nombres filas

rownames(df_ahorro)[1:10]
 [1] "Australia"  "Austria"    "Belgium"    "Bolivia"    "Brazil"    
 [6] "Canada"     "Chile"      "China"      "Colombia"   "Costa Rica"

Asignar nueva columa a la DF

df_ahorro$pais <- rownames(df_ahorro)

Crear Columna con Valor Promedio

Con los valores correspondientes a pop15 y pop75 obtener promedio por cada observación

df_ahorro$edad_promedio <- mean(c(df_ahorro$pop15, df_ahorro$pop75))

Inspección Nuevos Atributos

Revisar DF

head(df_ahorro, 5)
             sr pop15 pop75     dpi ddpi      pais edad_promedio
Australia 11.43 29.35  2.87 2329.68 2.87 Australia       18.6913
Austria   12.07 23.32  4.41 1507.99 3.93   Austria       18.6913
Belgium   13.17 23.80  4.43 2108.47 3.82   Belgium       18.6913
Bolivia    5.75 41.89  1.67  189.13 0.22   Bolivia       18.6913
Brazil    12.88 42.19  0.83  728.47 4.56    Brazil       18.6913

Funciones Valores Extremos

Mínimo en vector

min(df_ahorro$pop15)
[1] 21.44

Máximo en vector

max(df_ahorro$pop15)
[1] 47.64

Importar en Formato csv una Data Frame

df_gapminder <- read.csv('https://raw.githubusercontent.com/javendaXgh/datos/refs/heads/master/gapminder.csv') 
head(df_gapminder,3)
  X     country continent year lifeExp      pop gdpPercap
1 1 Afghanistan      Asia 1952  28.801  8425333  779.4453
2 2 Afghanistan      Asia 1957  30.332  9240934  820.8530
3 3 Afghanistan      Asia 1962  31.997 10267083  853.1007

👀: los datos tienen estructura tabular. Observaciones son filas y atributos las columnas

Estructura Gapminder

Estructura DF

str(df_gapminder)
'data.frame':   1704 obs. of  7 variables:
 $ X        : int  1 2 3 4 5 6 7 8 9 10 ...
 $ country  : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
 $ continent: chr  "Asia" "Asia" "Asia" "Asia" ...
 $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
 $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
 $ pop      : int  8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
 $ gdpPercap: num  779 821 853 836 740 ...

Tabla Sumario Gapminder

summary(df_gapminder)
       X            country           continent              year     
 Min.   :   1.0   Length:1704        Length:1704        Min.   :1952  
 1st Qu.: 426.8   Class :character   Class :character   1st Qu.:1966  
 Median : 852.5   Mode  :character   Mode  :character   Median :1980  
 Mean   : 852.5                                         Mean   :1980  
 3rd Qu.:1278.2                                         3rd Qu.:1993  
 Max.   :1704.0                                         Max.   :2007  
    lifeExp           pop              gdpPercap       
 Min.   :23.60   Min.   :6.001e+04   Min.   :   241.2  
 1st Qu.:48.20   1st Qu.:2.794e+06   1st Qu.:  1202.1  
 Median :60.71   Median :7.024e+06   Median :  3531.8  
 Mean   :59.47   Mean   :2.960e+07   Mean   :  7215.3  
 3rd Qu.:70.85   3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
 Max.   :82.60   Max.   :1.319e+09   Max.   :113523.1  

Tiene estructura tabular

Valores Únicos por Atributo

Países

unique(df_gapminder$country)
  [1] "Afghanistan"              "Albania"                 
  [3] "Algeria"                  "Angola"                  
  [5] "Argentina"                "Australia"               
  [7] "Austria"                  "Bahrain"                 
  [9] "Bangladesh"               "Belgium"                 
 [11] "Benin"                    "Bolivia"                 
 [13] "Bosnia and Herzegovina"   "Botswana"                
 [15] "Brazil"                   "Bulgaria"                
 [17] "Burkina Faso"             "Burundi"                 
 [19] "Cambodia"                 "Cameroon"                
 [21] "Canada"                   "Central African Republic"
 [23] "Chad"                     "Chile"                   
 [25] "China"                    "Colombia"                
 [27] "Comoros"                  "Congo, Dem. Rep."        
 [29] "Congo, Rep."              "Costa Rica"              
 [31] "Cote d'Ivoire"            "Croatia"                 
 [33] "Cuba"                     "Czech Republic"          
 [35] "Denmark"                  "Djibouti"                
 [37] "Dominican Republic"       "Ecuador"                 
 [39] "Egypt"                    "El Salvador"             
 [41] "Equatorial Guinea"        "Eritrea"                 
 [43] "Ethiopia"                 "Finland"                 
 [45] "France"                   "Gabon"                   
 [47] "Gambia"                   "Germany"                 
 [49] "Ghana"                    "Greece"                  
 [51] "Guatemala"                "Guinea"                  
 [53] "Guinea-Bissau"            "Haiti"                   
 [55] "Honduras"                 "Hong Kong, China"        
 [57] "Hungary"                  "Iceland"                 
 [59] "India"                    "Indonesia"               
 [61] "Iran"                     "Iraq"                    
 [63] "Ireland"                  "Israel"                  
 [65] "Italy"                    "Jamaica"                 
 [67] "Japan"                    "Jordan"                  
 [69] "Kenya"                    "Korea, Dem. Rep."        
 [71] "Korea, Rep."              "Kuwait"                  
 [73] "Lebanon"                  "Lesotho"                 
 [75] "Liberia"                  "Libya"                   
 [77] "Madagascar"               "Malawi"                  
 [79] "Malaysia"                 "Mali"                    
 [81] "Mauritania"               "Mauritius"               
 [83] "Mexico"                   "Mongolia"                
 [85] "Montenegro"               "Morocco"                 
 [87] "Mozambique"               "Myanmar"                 
 [89] "Namibia"                  "Nepal"                   
 [91] "Netherlands"              "New Zealand"             
 [93] "Nicaragua"                "Niger"                   
 [95] "Nigeria"                  "Norway"                  
 [97] "Oman"                     "Pakistan"                
 [99] "Panama"                   "Paraguay"                
[101] "Peru"                     "Philippines"             
[103] "Poland"                   "Portugal"                
[105] "Puerto Rico"              "Reunion"                 
[107] "Romania"                  "Rwanda"                  
[109] "Sao Tome and Principe"    "Saudi Arabia"            
[111] "Senegal"                  "Serbia"                  
[113] "Sierra Leone"             "Singapore"               
[115] "Slovak Republic"          "Slovenia"                
[117] "Somalia"                  "South Africa"            
[119] "Spain"                    "Sri Lanka"               
[121] "Sudan"                    "Swaziland"               
[123] "Sweden"                   "Switzerland"             
[125] "Syria"                    "Taiwan"                  
[127] "Tanzania"                 "Thailand"                
[129] "Togo"                     "Trinidad and Tobago"     
[131] "Tunisia"                  "Turkey"                  
[133] "Uganda"                   "United Kingdom"          
[135] "United States"            "Uruguay"                 
[137] "Venezuela"                "Vietnam"                 
[139] "West Bank and Gaza"       "Yemen, Rep."             
[141] "Zambia"                   "Zimbabwe"                

Años

unique(df_gapminder$year)
 [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

DPLYR (🏇🏽 de batalla)

library(dplyr)

DPLYR / Filtrado

Función filter.

df_venezuela <- df_gapminder %>%
  filter(country=='Venezuela')

df_venezuela
      X   country continent year lifeExp      pop gdpPercap
1  1633 Venezuela  Americas 1952  55.088  5439568  7689.800
2  1634 Venezuela  Americas 1957  57.907  6702668  9802.467
3  1635 Venezuela  Americas 1962  60.770  8143375  8422.974
4  1636 Venezuela  Americas 1967  63.479  9709552  9541.474
5  1637 Venezuela  Americas 1972  65.712 11515649 10505.260
6  1638 Venezuela  Americas 1977  67.456 13503563 13143.951
7  1639 Venezuela  Americas 1982  68.557 15620766 11152.410
8  1640 Venezuela  Americas 1987  70.190 17910182  9883.585
9  1641 Venezuela  Americas 1992  71.150 20265563 10733.926
10 1642 Venezuela  Americas 1997  72.146 22374398 10165.495
11 1643 Venezuela  Americas 2002  72.766 24287670  8605.048
12 1644 Venezuela  Americas 2007  73.747 26084662 11415.806

Revisar Tabla Venezuela

Tabla sumario

summary(df_venezuela)
       X          country           continent              year     
 Min.   :1633   Length:12          Length:12          Min.   :1952  
 1st Qu.:1636   Class :character   Class :character   1st Qu.:1966  
 Median :1638   Mode  :character   Mode  :character   Median :1980  
 Mean   :1638                                         Mean   :1980  
 3rd Qu.:1641                                         3rd Qu.:1993  
 Max.   :1644                                         Max.   :2007  
    lifeExp           pop             gdpPercap    
 Min.   :55.09   Min.   : 5439568   Min.   : 7690  
 1st Qu.:62.80   1st Qu.: 9318008   1st Qu.: 9307  
 Median :68.01   Median :14562164   Median :10025  
 Mean   :66.58   Mean   :15129801   Mean   :10089  
 3rd Qu.:71.40   3rd Qu.:20792772   3rd Qu.:10839  
 Max.   :73.75   Max.   :26084662   Max.   :13144  

Juntar DF´s

df_colombia <- df_gapminder %>%
  filter(country=='Colombia')

df_colombia
     X  country continent year lifeExp      pop gdpPercap
1  301 Colombia  Americas 1952  50.643 12350771  2144.115
2  302 Colombia  Americas 1957  55.118 14485993  2323.806
3  303 Colombia  Americas 1962  57.863 17009885  2492.351
4  304 Colombia  Americas 1967  59.963 19764027  2678.730
5  305 Colombia  Americas 1972  61.623 22542890  3264.660
6  306 Colombia  Americas 1977  63.837 25094412  3815.808
7  307 Colombia  Americas 1982  66.653 27764644  4397.576
8  308 Colombia  Americas 1987  67.768 30964245  4903.219
9  309 Colombia  Americas 1992  68.421 34202721  5444.649
10 310 Colombia  Americas 1997  70.313 37657830  6117.362
11 311 Colombia  Americas 2002  71.682 41008227  5755.260
12 312 Colombia  Americas 2007  72.889 44227550  7006.580

Combinar DF’s

Función bind_rows

df_gran_colombia <- bind_rows(df_venezuela,
                              df_colombia )

Revisar Nueva DF

df_gran_colombia
      X   country continent year lifeExp      pop gdpPercap
1  1633 Venezuela  Americas 1952  55.088  5439568  7689.800
2  1634 Venezuela  Americas 1957  57.907  6702668  9802.467
3  1635 Venezuela  Americas 1962  60.770  8143375  8422.974
4  1636 Venezuela  Americas 1967  63.479  9709552  9541.474
5  1637 Venezuela  Americas 1972  65.712 11515649 10505.260
6  1638 Venezuela  Americas 1977  67.456 13503563 13143.951
7  1639 Venezuela  Americas 1982  68.557 15620766 11152.410
8  1640 Venezuela  Americas 1987  70.190 17910182  9883.585
9  1641 Venezuela  Americas 1992  71.150 20265563 10733.926
10 1642 Venezuela  Americas 1997  72.146 22374398 10165.495
11 1643 Venezuela  Americas 2002  72.766 24287670  8605.048
12 1644 Venezuela  Americas 2007  73.747 26084662 11415.806
13  301  Colombia  Americas 1952  50.643 12350771  2144.115
14  302  Colombia  Americas 1957  55.118 14485993  2323.806
15  303  Colombia  Americas 1962  57.863 17009885  2492.351
16  304  Colombia  Americas 1967  59.963 19764027  2678.730
17  305  Colombia  Americas 1972  61.623 22542890  3264.660
18  306  Colombia  Americas 1977  63.837 25094412  3815.808
19  307  Colombia  Americas 1982  66.653 27764644  4397.576
20  308  Colombia  Americas 1987  67.768 30964245  4903.219
21  309  Colombia  Americas 1992  68.421 34202721  5444.649
22  310  Colombia  Americas 1997  70.313 37657830  6117.362
23  311  Colombia  Americas 2002  71.682 41008227  5755.260
24  312  Colombia  Americas 2007  72.889 44227550  7006.580

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